University of Notre Dame
College of Business
Notre Dame, IN 46556
Institutional Affiliation: Washington University in St. Louis
NBER Working Papers and Publications
|November 2019||Estimating The Anomaly Base Rate|
with , : w26493
The academic literature literally contains hundreds of variables that seem to predict the cross-section of expected returns. This so-called "anomaly zoo" has caused many to question whether researchers are using the right tests of statistical significance. But, here's the thing: even if researchers use the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors---i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly.
So, what are the right priors? What is the correct anomaly base rate?
We develop a first way to estimate the anomaly base rate by combining two key insights: 1) Empirical-Bayes methods capture the implicit process by which researchers form priors b...
|June 2018||Monetary Momentum|
with : w24748
We document a large return drift around monetary policy announcements by the Federal Open Market Committee (FOMC). Stock returns start drifting up 25 days before expansionary monetary policy surprises, whereas they decrease before contractionary surprises. The cumulative return difference across expansionary and contractionary policy decisions amounts to 2.5% until the day of the policy decision and continues to increase to more than 4.5% 15 days after the meeting. Standard returns factors and time-series momentum do not span the return drift around FOMC policy decisions. The return drift is a market-wide phenomenon and holds for all industries and many international equity markets. A simple trading strategy exploiting the drift around FOMC meetings increases Sharpe ratios relative to a bu...
|March 2017||Dissecting Characteristics Nonparametrically|
with , : w23227
We propose a nonparametric method to test which characteristics provide independent information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large number of characteristics, allows for a flexible functional form, and is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns, and nonlinearities are important. Our proposed method has higher out-of-sample explanatory power compared to linear panel regressions, and increases Sharpe ratios by 50%.
Published: Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew Karolyi, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, vol 33(5), pages 2326-2377. citation courtesy of
|November 2016||Monetary Policy and the Stock Market: Time-Series Evidence|
with : w22831
The slope factor is constructed from changes in federal funds futures of different horizons and predicts stock returns at the weekly frequency: faster policy easing positively predicts returns. It contains information about the speed of future monetary policy tightening and loosening, and predicts changes in interest rates and forecast revisions of professional forecasters. The tone of speeches by FOMC members correlates with the slope factor. The predictive power concentrates in times of high uncertainty in line with the pre-FOMC announcement drift. Our findings show the path of interest rates matters for asset prices, and monetary policy affects asset prices continuously.